Development of a Health Impact Assessment Screening Tool: A Value Versus Investment Approach
Bibliographic record
Abstract
The first step of any health impact assessment (HIA) is screening to determine whether an HIA is an appropriate assessment option. Although screening tools exist, there is no universally-applied, transparent method that includes consideration of costs (investment) and benefits (value) of the HIA process. An HIA screening tool was developed to help address this need through the use of a targeted scoring system to assess the value of conducting an HIA against the required investment. The tool was subject to both internal and external testing. Individuals from eight different countries agreed to participate in testing of the HIA tool. Overall, there was a high level of agreement amongst participants regarding the investment versus value scoring as well as the screening outcome. Ultimately, the iterative development process, along with internal and external testing of the HIA screening tool, proved successful and demonstrates its applicability to a variety of scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.116 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".